After laser powder bed fusion (LPBF) of an ultra-strong in situ TiC whisker reinforced β-Ti composite, this paper investigates the evolution of microstructure and mechanical properties in response to heat treatment at different temperatures. Using in depth nano-SEM and TEM analyses, it is shown that ageing at 400 °C rounds the whiskers, annihilates the strain fields and grows Mo segregated nano-cells, but without improving the ductility. In contrast, ageing at 600 °C enables the transformation of metastable β to a lamellar β + α, leading to a dual phase matrix embedding TiC particles. This is in such a manner that extra ageing at 600 °C coalesces the nano-lamellar α + β microstructure to form a coarser micro-lamellar α + β matrix. This microstructure achieves 66 % of the compressive deformation of Cp-Ti, and over 1400 MPa compressive strength after 1 h of ageing at 600 °C. Despite this success under compression, hard and stiff TiC particles may still cause large spherical fractured voids, severely limiting the plastic deformation under tension.

This paper studies a distributed online optimization problem over partially Free-In and Free-Out (FIFO) networks, in which a set of unfixed agents cooperate to minimize the sum of a group of time-varying functions over a time horizon. To be specific, the agents are divided into static agents and dynamic agents. The static agents are those who remain in the network during the whole time horizon, while the dynamic agents are allowed to join and leave the network freely. Based on the dual averaging technique, two novel distributed algorithms are developed to address the distributed optimization problem in such a dynamic environment. In the case where agents can distinguish whether their out-neighbors are dynamic agents or static agents, a weighting matrix based algorithm is developed. In the case where the identities of out-neighbors are unavailable, a gradient-storage based algorithm is developed, which has higher communication and local storage requirements than the weighting matrix based algorithm. By assuming that each dynamic agent has at least one in-neighbor and one out-neighbor who are static agents, the running average regret is shown to be upper bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{\mathit {T}}})$</tex-math></inline-formula> under suitable step-sizes, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit {T}$</tex-math></inline-formula> is the time horizon. A simulation study on binary classification is given to verify the effectiveness of the developed algorithms. In addition, a numerical example is used to highlight the advantages of the proposed methods over the distributed sub-gradient decent method.

Game theory has emerged as a fundamental framework for modeling and analyzing strategic interactions and decision-making among multiple agents, and has witnessed rapidly growing impact in cyber–physical systems over the past decade. Its integration with dynamic systems has driven major theoretical and technological advances in a wide range of applications, including smart grids, autonomous driving, robotic swarms, and networked control systems. In particular, distributed games in dynamic systems and their equilibrium learning mechanisms have attracted increasing attention due to their scalability, lightweight information exchange, and real-time implementability. This article provides a comprehensive survey of distributed games in dynamic systems, where agents interact only with local neighbors while collectively achieving global equilibrium and stability. First, the foundational theories of distributed dynamic games under three representative classes of systems: linear dynamic systems, nonlinear dynamic systems, and uncertain dynamic systems, are presented. Then, state-of-the-art distributed equilibrium learning and control methods are reviewed, including gradient-based dynamics, payoff-based learning, best-response dynamics, and learning-based approaches. To demonstrate the practical relevance and impact of distributed games in dynamic systems, representative application domains are discussed in detail. Finally, several promising future research directions are outlined, highlighting open challenges at the intersection of distributed games, learning, and dynamic systems.

Distributed optimization, as a key technology for collaborative intelligence in multiagent systems, has been widely applied in sensor networks, deep learning, and smart grids. Although numerous effective algorithms have been proposed, classical methods typically rely on idealized assumptions, such as accurate objective information, perfect communication channels, and trustworthy system environments. However, these assumptions are frequently violated in real-world applications. To bridge the gap between theory and practice, distributed optimization under information constraints has emerged as a research focus. This survey provides a systematic overview of recent advances in this field. We categorize information constraints based on their origin into three primary types: i) observational constraints, including stochastic objectives, online optimization, and zeroth-order methods; ii) communication constraints, such as random network topologies, delays, asynchronous updates, and communication-efficient strategies; and iii) system-level constraints, encompassing privacy preservation and Byzantine-resilient optimization. This survey reviews the research progress and challenges associated with each constraint category. Furthermore, we use two representative case studies to analyze the practical application of these algorithms and the origins of information constraints in real-world problems. Finally, we explore promising future research directions.

Manual grading of dental students’ embroidery assignments is not only labor-intensive but also subjective. To address these limitations, our study proposes an automated grading model based on ResNet-50 architecture enhanced with a multi-region aggregation mechanism. This approach aims to standardize the grading process, improve fairness and efficiency in assessment. A total of 381 embroidery assignment images were collected from the 2020–2023 student cohorts. The 2022 cohort was designated as an external test set to assess model generalization with different data distributions. We proposed a multi-region aggregation mechanism based on ResNet-50 and compared two aggregation strategies: multi-head attention (MHA) aggregation and average weighting (AW) aggregation. VGG-16, DenseNet-121, ViT, and ResNet-50 were considered as baseline models. All models were trained using 5-fold cross-validation, employing a weighted CrossEntropyLoss to address class imbalance, with evaluation metrics including accuracy, precision, recall, and F1 score. The ResNet-50 AW model achieved the highest test accuracy of 80% on the test set, while the ViT and the VGG-16 models achieved 75%, second to ResNet-50 AW. Although models’ performance degraded on the external test set, ResNet-50 AW maintained the highest accuracy of 64% and reduced misclassifications of grade B and C samples. Despite excelling on the validation set, ResNet-50 MHA showed similar performance to ResNet-50 on the test set. ViT and VGG-16 achieved higher accuracy for grade A on both the test set and the external test set. The ResNet-50 AW model highlights the potential of deep learning methods to automate the grading of artistic assignments via a multi-region aggregation mechanism. Further validation of the model’s generalization is needed. Future work should improve dataset quality and diversity and enhance system interpretability to refine the grading process for greater accuracy and transparency.

This study investigated the effects of low and high polarity extracts (LPE and HPE) of Angelica sinensis (AS) on growth, body composition, and metabolism in carp ( Cyprinus carpio var. Jian). Over a 42‐day period, 780 fish were randomly separated into 13 groups with three replicate aquariums respectively. Thirteen groups received the feeding of basic diet, six LPE diets, and six HPE diets, respectively. The results indicated that dietary LPE decreased weight gain (WG), condition factor (CF), the activities of Na + , K + ‐ATPase, γ‐glutamyl transpeptidase (γ‐GT), trypsin and lipase in digestive organs, plasma total amino acids (TAAs), triglyceride (TG) and ammonia levels, lipid productive value (LPV), and ammonia excretion rate (AER; p &lt; 0.05), increased the activities of alkaline phosphatase (AKP), glutamate‐oxaloacetate transaminase (GOT), glutamate‐pyruvate transaminase (GPT), and α‐amylase in digestive organs, the content of total protein (TP) and glucose (GLU) in plasma, protein productive value (PPV), oxygen consumption rate (OCR), and O:N ratio in carp ( p &lt; 0.05). At the same time, dietary HPE increased WG, CF, the activities of lactate dehydrogenase (LDH), trypsin and lipase in digestive organs, the content of TP in plasma and LPV, PPV, OCR, and O:N ratio ( p &lt; 0.05) and decreased the activities of GOT and GPT in hepatopancreas as well as the content of ammonia, TAA, and GLU in plasma of carp ( p &lt; 0.05). According to the above findings, dietary LPE inhibits the growth and accumulation of body lipid and enhances the accumulation of body proteins by decreasing the digestion and absorption of lipids as well as amino acid catabolism, and increasing the catabolism of sugar and fat in fish. Dietary HPE enhances the growth and accumulation of body lipid and proteins by decreasing the catabolism of amino acid and increasing the digestion and absorption of proteins and lipids and the catabolism of sugar in fish.

Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and interaction dynamics that remote sensors cannot replace. However, unimodal tactile perception is inherently limited by its sparse spatial coverage and lack of global semantic context. With the recent explosion in deep learning and large language models, integrating tactile with vision and language has become essential to bridge physical interaction with semantic reasoning, leading to the emergence of Multimodal Tactile Fusion. Despite rapid progress, the existing researches remain fragmented across disparate datasets, sensing modalities, and tasks, lacking a unified theoretical framework. To address this gap, this paper provides a comprehensive survey of multimodal tactile fusion research up to the first quarter of 2026. We propose a hierarchical taxonomy that organizes the field into two primary dimensions: multimodal datasets and multimodal methods. On the data side, we categorize resources ranging from Tactile-Vision datasets, Tactile-Language datasets, Tactile-Vision-Language datasets, and Tactile-Vision-Other datasets. On the method side, we structure prior work into three core pillars: (1) Multimodal Perception and Recognition, which focuses on object understanding and grasp prediction; (2) Cross-Modal Generation, focusing on bidirectional translation between tactile, vision, and text; and (3) Multimodal Interaction, emphasizing feedback control and language-guided manipulation. Furthermore, we summarize representative tactile sensing hardware, review commonly used evaluation metrics and benchmark settings, and discuss current challenges and promising future directions.

This paper is concerned with the problem of designing distributed event-triggered H∞ filters over sensor networks subject to heterogeneous coupling intercommunication delays. A new distributed event-triggered scheme is proposed to determine whether or not each sensor's current sampled data should be broadcasted and transmitted to its underlying neighboring nodes through the communication network. In this scheme, each sensor node is able to make its own decisions to broadcast and transmit only when its local measurement output error exceeds a designed threshold. Heterogeneous coupling delays are incorporated in the intercommunication between the specific sensor node and its interacting neighbors. A refined technique is proposed to realize the complicated decoupling among the exchanged measurement outputs in the presence of coupling intercommunication delays. Then the resulting filter error system is modeled by a new delay system subject to finite time-varying 'state' delays. Based on the Lyapunov-Krasovskii functional method, a sufficient condition for distributed event-triggered H∞ filter design is established, from which the desired filter parameters and the triggering parameter in the event condition can be co-designed. The filter design problem is posed in terms of linear matrix inequalities. A quarter-car suspension model is finally presented to show the effectiveness and feasibility of the developed theoretical results.